Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs

Replication code for training Quantum Neural Networks using entangled datasets. This is the version of the code that was used to generate the experiment results in the related publication. For future developments and discussion see the Github repository. Experiments: avg_rank_exp.py: Experiments for training QNNs using training data of varying Schmidt rank nlihx_exp.py: Experiments for training QNNs using linearly dependent data ortho_exp.py: Experiments for training QNNs using orthogonal training data Visualisation/Analysis of data (plots.py): - Generates plots for the experiments above either from the data in experimental_results or from the processed results (see Data). - Processes results to extract information from raw data in experimental_results (to change behavior see the function calls at the end of plots.py). Data: The raw data for the experiments is available in the experiment dataset.

复现训练量子神经网络所使用的纠缠数据集的代码。此代码版本被用于生成相关出版物中的实验结果。关于未来的发展和讨论，请参阅 Github 仓库。实验：avg_rank_exp.py：使用不同施密特秩的训练数据训练量子神经网络的实验；nlihx_exp.py：使用线性相关数据训练量子神经网络的实验；ortho_exp.py：使用正交训练数据训练量子神经网络的实验。数据可视化/分析（plots.py）：- 从实验结果中的数据或处理后的结果（参见数据）生成上述实验的图表。- 处理结果以从实验结果中的原始数据中提取信息（若要更改行为，请参阅 plots.py 文件末尾的函数调用）。数据：实验的原始数据存储在实验数据集中。